کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
415833 681240 2012 18 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A semiparametric Bayesian approach to generalized partial linear mixed models for longitudinal data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
A semiparametric Bayesian approach to generalized partial linear mixed models for longitudinal data
چکیده انگلیسی

A generalized partial linear mixed model (GPLMM) is a natural extension of generalized linear mixed models (GLMMs) and partial linear models (PLMs). Almost all existing methods for analyzing GPLMMs are developed on the basis of the assumption that random effects are distributed as a fully parametric distribution such as normal distribution. In this paper, we extend the GPLMMs by specifying a Dirichlet process prior for a general distribution of random effects, and propose a semiparametric Bayesian approach by simultaneously utilizing an approximation truncation Dirichlet process prior of the random effects and a PP-spline approximation of the smoothing function. By combining the block Gibbs sampler and the Metropolis–Hastings algorithm, a hybrid algorithm is presented for sampling observations from the posterior distribution. A procedure for selecting the degree of the polynomial components in nonparametric approximation using Bayes factor is given via path sampling. Some goodness-of-fit statistics are proposed to evaluate the plausibility of the posited model. Several simulation studies and a real example are presented to illustrate the proposed methodologies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computational Statistics & Data Analysis - Volume 56, Issue 12, December 2012, Pages 4348–4365
نویسندگان
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